




"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a_        =       {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a_        =       {
    'sample_size': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a_        =       {
    'sample_size': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a_        =       {
    'num_train_timesteps': 4_0,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a_        =       {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

a_        =       {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}







def      __UpperCAmelCase     ( __UpperCamelCase	):
			if isinstance(__UpperCamelCase	,				__UpperCamelCase	):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError('''boolean value expected'''	)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False	):
			__lowercase			:  Optional[int]								=       checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
			__lowercase			:  Union[str, Any]								=       checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
			__lowercase			:  List[str]								=       checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
			__lowercase			:  List[str]								=       checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
			__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
			__lowercase			:  str								=       checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
			__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
			__lowercase			:  Optional[int]								=       checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
			__lowercase			:  Dict								=       checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
			__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

			if has_skip:
						__lowercase			:  Optional[Any]								=       checkpoint[f"""{old_prefix}.skip_connection.weight"""]
						__lowercase			:  int								=       checkpoint[f"""{old_prefix}.skip_connection.bias"""]

			return new_checkpoint







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=None	):
			__lowercase			,__lowercase			,__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3	,				dim=0	)
			__lowercase			,__lowercase			,__lowercase			:  List[Any]								=       checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3	,				dim=0	)

			__lowercase			:  List[str]								=       checkpoint[f"""{old_prefix}.norm.weight"""]
			__lowercase			:  Tuple								=       checkpoint[f"""{old_prefix}.norm.bias"""]

			__lowercase			:  Tuple								=       weight_q.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       bias_q.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       weight_k.squeeze(-1	).squeeze(-1	)
			__lowercase			:  Optional[int]								=       bias_k.squeeze(-1	).squeeze(-1	)
			__lowercase			:  int								=       weight_v.squeeze(-1	).squeeze(-1	)
			__lowercase			:  str								=       bias_v.squeeze(-1	).squeeze(-1	)

			__lowercase			:  List[str]								=       (
			    checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1	).squeeze(-1	)
			)
			__lowercase			:  int								=       checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1	).squeeze(-1	)

			return new_checkpoint







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  int								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)
			__lowercase			:  Union[str, Any]								=       {}

			__lowercase			:  Union[str, Any]								=       checkpoint['''time_embed.0.weight''']
			__lowercase			:  List[Any]								=       checkpoint['''time_embed.0.bias''']
			__lowercase			:  Dict								=       checkpoint['''time_embed.2.weight''']
			__lowercase			:  Optional[int]								=       checkpoint['''time_embed.2.bias''']

			if unet_config["num_class_embeds"] is not None:
						__lowercase			:  Optional[Any]								=       checkpoint['''label_emb.weight''']

			__lowercase			:  Any								=       checkpoint['''input_blocks.0.0.weight''']
			__lowercase			:  str								=       checkpoint['''input_blocks.0.0.bias''']

			__lowercase			:  Dict								=       unet_config['''down_block_types''']
			__lowercase			:  str								=       unet_config['''layers_per_block''']
			__lowercase			:  Tuple								=       unet_config['''attention_head_dim''']
			__lowercase			:  List[str]								=       unet_config['''block_out_channels''']
			__lowercase			:  Tuple								=       1
			__lowercase			:  Optional[Any]								=       channels_list[0]

			for i, layer_type in enumerate(__UpperCamelCase	):
						__lowercase			:  List[Any]								=       channels_list[i]
						__lowercase			:  List[Any]								=       current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(__UpperCamelCase	):
												__lowercase			:  Dict								=       f"""down_blocks.{i}.resnets.{j}"""
												__lowercase			:  Union[str, Any]								=       f"""input_blocks.{current_layer}.0"""
												__lowercase			:  List[Any]								=       True if j == 0 and downsample_block_has_skip else False
												__lowercase			:  Union[str, Any]								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				has_skip=__UpperCamelCase	)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(__UpperCamelCase	):
												__lowercase			:  str								=       f"""down_blocks.{i}.resnets.{j}"""
												__lowercase			:  int								=       f"""input_blocks.{current_layer}.0"""
												__lowercase			:  int								=       True if j == 0 and downsample_block_has_skip else False
												__lowercase			:  Dict								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				has_skip=__UpperCamelCase	)
												__lowercase			:  Any								=       f"""down_blocks.{i}.attentions.{j}"""
												__lowercase			:  Dict								=       f"""input_blocks.{current_layer}.1"""
												__lowercase			:  Dict								=       convert_attention(
												    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
												current_layer += 1

						if i != len(__UpperCamelCase	) - 1:
									__lowercase			:  int								=       f"""down_blocks.{i}.downsamplers.0"""
									__lowercase			:  Optional[int]								=       f"""input_blocks.{current_layer}.0"""
									__lowercase			:  int								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
									current_layer += 1

						__lowercase			:  Any								=       current_channels

			# hardcoded the mid-block for now
			__lowercase			:  List[Any]								=       '''mid_block.resnets.0'''
			__lowercase			:  int								=       '''middle_block.0'''
			__lowercase			:  Optional[int]								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
			__lowercase			:  str								=       '''mid_block.attentions.0'''
			__lowercase			:  Any								=       '''middle_block.1'''
			__lowercase			:  int								=       convert_attention(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
			__lowercase			:  Union[str, Any]								=       '''mid_block.resnets.1'''
			__lowercase			:  Union[str, Any]								=       '''middle_block.2'''
			__lowercase			:  int								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			__lowercase			:  Optional[int]								=       0
			__lowercase			:  Optional[int]								=       unet_config['''up_block_types''']

			for i, layer_type in enumerate(__UpperCamelCase	):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1	):
												__lowercase			:  int								=       f"""up_blocks.{i}.resnets.{j}"""
												__lowercase			:  List[str]								=       f"""output_blocks.{current_layer}.0"""
												__lowercase			:  Tuple								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				has_skip=__UpperCamelCase	)
												current_layer += 1

									if i != len(__UpperCamelCase	) - 1:
												__lowercase			:  Union[str, Any]								=       f"""up_blocks.{i}.upsamplers.0"""
												__lowercase			:  Tuple								=       f"""output_blocks.{current_layer-1}.1"""
												__lowercase			:  List[str]								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1	):
												__lowercase			:  Union[str, Any]								=       f"""up_blocks.{i}.resnets.{j}"""
												__lowercase			:  List[str]								=       f"""output_blocks.{current_layer}.0"""
												__lowercase			:  Any								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				has_skip=__UpperCamelCase	)
												__lowercase			:  Tuple								=       f"""up_blocks.{i}.attentions.{j}"""
												__lowercase			:  str								=       f"""output_blocks.{current_layer}.1"""
												__lowercase			:  Dict								=       convert_attention(
												    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
												current_layer += 1

									if i != len(__UpperCamelCase	) - 1:
												__lowercase			:  List[str]								=       f"""up_blocks.{i}.upsamplers.0"""
												__lowercase			:  Dict								=       f"""output_blocks.{current_layer-1}.2"""
												__lowercase			:  Dict								=       convert_resnet(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			__lowercase			:  Tuple								=       checkpoint['''out.0.weight''']
			__lowercase			:  List[Any]								=       checkpoint['''out.0.bias''']
			__lowercase			:  str								=       checkpoint['''out.2.weight''']
			__lowercase			:  List[Any]								=       checkpoint['''out.2.bias''']

			return new_checkpoint


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()

			parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
			parser.add_argument(
			    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
			)
			parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

			a_        =       parser.parse_args()
			a_        =       strabool(args.class_cond)

			a_        =       os.path.basename(args.unet_path)
			print(F"Checkpoint: {ckpt_name}")

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						a_        =       IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						a_        =       LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						a_        =       TEST_UNET_CONFIG
			else:
						raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

			if not args.class_cond:
						a_        =       None

			a_        =       con_pt_to_diffuser(args.unet_path, unet_config)

			a_        =       UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						a_        =       CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						a_        =       CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						a_        =       CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

			a_        =       CMStochasticIterativeScheduler(**scheduler_config)

			a_        =       ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)







"""simple docstring"""



from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class    __a						:
				pass